The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such ‘free’ model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process – the genetic algorithm (GA) – has become especially popular. As applied to modeling, GAs represent a Darwinian process – an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as ‘parents’ who pass their parameters onto a new generation. At the Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to ‘score’ the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions.

Impact study of integrated precipitable water estimated from Indian GPS measurements

The Global Positioning System – Integrated Precipitable Water (IPW) data from Indian stations namely Chennai, Guwahati, Kolkata, Mumbai and New Delhi have been assimilated in the National Centre for Medium Range Weather Forecasting’s (NCMRWF) Global Data Assimilation System (GDAS). Gridpoint Statistical Interpolation (GSI) Scheme of GDAS analysis is experimented with the global model T254L64. The analyses and forecasts are carried out at triangular truncation of wave number 254 and with 64 levels in vertical. Global analyses are carried four times (0000 UTC, 0600 UTC, 1200 UTC and 1800 UTC) daily with intermittent time scheme. Model integrations are carried up to 168 hours. The present study examines the impact that integrated precipitable water has over various meteorological parameters. The study reveals that the assimilation of IPW data influences the analyses and corresponding forecasts of the weather model T254L64. This is an attempt of assimilation of IPW data of the aforesaid five Indian stations in the global model and examination of corresponding impact on various meteorological parameters over Indian region. It is seen that for the layers above 750 hPa the zonal and meridional wind components for IPW analyses have less biases. Forecasts from IPW simulations are found to have consistently by lower 850 hPa wind vector root mean square error (RMSE) where as at 250 hPa, improvement in IPW runs are seen only for day-1 and day-4 forecasts. For temperature at 850 hPa, IPW forecasts valid for day-4 & day-5 are better. At 250 hPa, temperature RMSE for IPW runs is lower for day-1 forecasts. Mean error of IPW forecasts at 250 hPa is lower for all the days of forecasts. Also, geo-potential RMSE for the IPW runs at 250 hPa is lower for all the days of forecasts. Forecasts vs analyses study shows positive impact of IPW assimilation on the anomaly and pattern correlations.

Building a comprehensive dataset for the validation of daylight simulation software, using complex "real architecture"

<p>This research focused on building a comprehensive dataset for use in validation studies of daylight simulation software. The aim of the set is to add to existing validation data to better cover a wide range of complexities and weather conditions. This will allow for not only the validation of simulation software, but the comparison of multiple simulators in their general strengths and weaknesses as well as feasibility for early ‘sketch’ design stages and complete building simulations. The set can also aid in the creation of valid simulation parameter starting points for designers. The research examined the current ‘gold standard’ validation dataset from the BRE-IDMP, and found that while it provides excellent validation opportunities for simulators that can support its detailed patch-based sky model; an equally high quality dataset is needed for simulators that support more simplified skies. This is essential as most of the weather data for annual daylighting simulations available to designers, such as the US-DOE’s collection of TMY data, can only be used in mathematical sky models such as the Perez all-weather model. It is also essential that real world, complex light-path scenarios commonly found in buildings be addressed by validation in addition to the simple single room, single opening tests which are prevalent in the daylight simulation field. A dataset suite is proposed, similar to the BESTEST suite for energy simulation, which covers basic analytical test cases for lighting simulators, simple office scenarios and a complex shaded classroom in a tropical climate. The dataset is valuable for the testing of daylight simulators which make use of the common CIE general or Perez all-weather skies. These datasets were used in a trial validation of Autodesk’s 3ds Max Design and Radiance, which included significant sensitivity testing of the two empirical datasets included in the suite. This demonstrated the usefulness of each dataset, and any issues with their data. It also highlighted the key inputs of any simulation model where designers must take significant care.</p>

Building a comprehensive dataset for the validation of daylight simulation software, using complex "real architecture"

<p>This research focused on building a comprehensive dataset for use in validation studies of daylight simulation software. The aim of the set is to add to existing validation data to better cover a wide range of complexities and weather conditions. This will allow for not only the validation of simulation software, but the comparison of multiple simulators in their general strengths and weaknesses as well as feasibility for early ‘sketch’ design stages and complete building simulations. The set can also aid in the creation of valid simulation parameter starting points for designers. The research examined the current ‘gold standard’ validation dataset from the BRE-IDMP, and found that while it provides excellent validation opportunities for simulators that can support its detailed patch-based sky model; an equally high quality dataset is needed for simulators that support more simplified skies. This is essential as most of the weather data for annual daylighting simulations available to designers, such as the US-DOE’s collection of TMY data, can only be used in mathematical sky models such as the Perez all-weather model. It is also essential that real world, complex light-path scenarios commonly found in buildings be addressed by validation in addition to the simple single room, single opening tests which are prevalent in the daylight simulation field. A dataset suite is proposed, similar to the BESTEST suite for energy simulation, which covers basic analytical test cases for lighting simulators, simple office scenarios and a complex shaded classroom in a tropical climate. The dataset is valuable for the testing of daylight simulators which make use of the common CIE general or Perez all-weather skies. These datasets were used in a trial validation of Autodesk’s 3ds Max Design and Radiance, which included significant sensitivity testing of the two empirical datasets included in the suite. This demonstrated the usefulness of each dataset, and any issues with their data. It also highlighted the key inputs of any simulation model where designers must take significant care.</p>

Aerosol-cloud-turbulence interactions in well-coupled Arctic boundary layers over open water

Late springtime Arctic mixed-phase convective clouds over open water in the Fram Strait as observed during the recent ACLOUD field campaign are simulated at turbulence-resolving resolutions. The main research objective is to gain more insight into the coupling of these cloud layers to the surface, and into the role played by interactions between aerosol, hydrometeors and turbulence in this process. A composite case is constructed based on data collected by two research aircraft on 18 June 2017. The boundary conditions and large-scale forcings are based on weather model analyses, yielding a simulation that freely equilibrates towards the observed thermodynamic state. The results are evaluated against a variety of independent aircraft measurements. The observed cloud macro- and microphysical structure is well reproduced, consisting of a stratiform cloud layer in mixed-phase fed by surface-driven convective transport in predominantly liquid phase. Comparison to noseboom turbulence measurements suggests that the simulated cloud-surface coupling is realistic. A joint-pdf analysis of relevant state variables is conducted, suggesting that locations where the mixed-phase cloud layer is strongly coupled to the surface by convective updrafts act as hot-spots for invigorated interactions between turbulence, clouds and aerosol. A mixing-line analysis reveals that the turbulent mixing is similar to warm convective cloud regimes, but is accompanied by hydrometeor transitions that are unique for mixed-phase cloud systems. Distinct fingerprints in the joint-pdf diagrams also explain i) the typical ring-like shape of ice mass in the outflow cloud deck, ii) its slightly elevated buoyancy, and iii) an associated local minimum in CCN.

Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking with simulation data

We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.

GIS based pest-weather model to predict the incidence of Girdle beetle (Oberiopsis brevis) in Soybean crop

Girdle beetle (Oberiopsis brevis) is an important insect of soybean that can cause up to 42.2% yield loss in severe infestation during flowering stage. The infestation of girdle beetle is prevailed by congenial environmental conditions, which leads girdle beetle to be the severe pest of soybean. The present study assesses the relevant weather variables that can cause the peak infestation. Crop Pest Surveillance and Advisory Project (CROPSAP) survey data of girdle beetle incidence were analyzed with weather variables using correlation and regression techniques. The girdle beetle infestation had significantly positive correlation with relative humidity of current and 2nd lag week (RH0, RH-2); and with rainfall of 2nd lag week (RF-2) but significantly negative correlation with maximum temperature of 1st lag week (TMax-1). The multiple regression technique was used to develop the forewarning models for three zones (Vidarbha, Madhya Maharashtra and Marathwada zones) and overall Maharashtra, the developed models could explain 80.30%, 94.62%, 73.56% and 79.56% variation in girdle beetle infestation, respectively. The congenial conditions for the peak infestation of girdle beetle on soybean have been worked out and validated, which were TMax0, RH0, RF0, RH-1, RF-1, TMax-2, and RF-2 ranged between 28.6-31.6 ºC, 85.2- 91.8 %, 31.8-119.2 mm, 86.3-92.6 %, 38.1-76.4 mm, 27.7-30.8ºC, and 23.3-60.7 mm, respectively. The insect forewarning would be useful in devising the integrated management strategies for protecting the crop from insect in the incidence region.